Classify

classify takes a DataFrame and a list of allowed categories, then assigns each row to exactly one category using web research that scales to the difficulty of the classification. Supports binary (yes/no) and multi-category classification with optional reasoning output.

Examples

GICS sector classification

from pandas import DataFrame
from everyrow.ops import classify

companies = DataFrame([
    {"company": "Apple"},
    {"company": "JPMorgan Chase"},
    {"company": "ExxonMobil"},
    {"company": "Pfizer"},
    {"company": "Procter & Gamble"},
    {"company": "Tesla"},
    {"company": "AT&T"},
    {"company": "Caterpillar"},
    {"company": "Duke Energy"},
    {"company": "Simon Property Group"},
])

result = await classify(
    task="Classify this company by its GICS industry sector",
    categories=[
        "Energy", "Materials", "Industrials", "Consumer Discretionary",
        "Consumer Staples", "Health Care", "Financials",
        "Information Technology", "Communication Services",
        "Utilities", "Real Estate",
    ],
    input=companies,
)
print(result.data[["company", "classification"]])

Output:

Binary classification

For yes/no questions, use two categories:

result = await classify(
    task="Is this company founder-led?",
    categories=["yes", "no"],
    input=companies,
)

Custom output column and reasoning

result = await classify(
    task="Classify each company by its primary industry sector",
    categories=["Technology", "Finance", "Healthcare", "Energy"],
    input=companies,
    classification_field="sector",
    include_reasoning=True,
)
print(result.data[["company", "sector", "reasoning"]])

Parameters

Output

One column is added to each input row (name controlled by classification_field):

Via MCP

MCP tool: everyrow_classify